A more globalised and knowledgeable consumer base expects more from the businesses it chooses to spend money with. Products, services and shopping experiences which are not engaging, seamless or offer added value simply do not make the cut.
For businesses and brands, these changing demands necessitate close inspection of current offerings to identify if there are elements of their set-ups that fall short of the mark – and, crucially, how they can be optimised to take advantage of a changing consumer landscape. How streamlined is the customer journey? Is the overall user experience sufficiently personalised? Are marketing efforts having their intended outcomes?
Failing to do so can mean losing out on potential customers, business, market share and profit.
AI is revolutionising the world of marketing. With the power to transform operational efficiency and personalise the customer experience – in turn, emphasising and improving the human element of a business – it is no surprise that marketing teams are leveraging its potential.
With the prevalence of mobile, social media and real-time apps, and technology changing the ways in which people shop, connect, share and communicate, modern consumers are always “on”. Customers are no longer numbers: they are individuals with unique interests, thoughts and preferences, primed for brands to engage with on a more human level. Increasingly, AI technology is so advanced, that predictive analytics are able to anticipate what a target audience wants before it even knows. Now, with big data and AI at their fingertips, marketers can capitalise on the shift from marketing automation to personalisation. By combining automated data collection with behavioural targeting, brands are in a much stronger position to achieve their business goals.
The central tenet of AI is optimisation: ensuring that the right content is shown to the right people at the right time. Brian Solis, Principal Analyst at Altimeter, describes AI marketing as combining “intelligent tech and human creativity to learn, understand and engage with consumers at the individual level with hyper-personalised, relevant and timely communications, making them feel compelled to remain engaged.”
But that is not its only benefit. As Verizon indicates, the widespread benefits of AI in digital marketing include:
- Maximising customer data collection
- Visualising customer journeys
- Researching customers
- Personalising content
- Boosting customer engagement
- Identifying and forecasting data trends more quickly
- Delivering a more convenient customer experience
- Freeing up human resources for more creative, critical-thinking tasks
AI marketing relies on techniques such as algorithms, machine learning, and deep learning to make individualised offers and suggestions in real-time. Its use cases also encompass data analysis, content generation, natural language processing, automated decision making and media buying.
These technologies are now so integrated in our daily lives – from social media platforms to household tech devices to mobile apps – that most clicks, interactions and decisions are, in some way, linked to AI. Common, everyday examples include: curated product recommendations on e-commerce sites; customer service chatbots on banking apps; Siri helping to streamline personal and professional lives; Uber’s real-time dynamic pricing; and vehicle sat-navs automatically rerouting journeys to avoid traffic congestion.
There are three core elements of AI:
- Machine learning. Machine learning uses AI and computer algorithms to analyse information and make automatic improvements based on experience. Any new information is analysed through the lens of relevant, historical data; based on what has or has not worked in the past, machine learning systems will then make informed decisions. For example, machine learning correctly categorises certain emails as “junk” or “spam” based on interactions with previous emails, or the content that these emails contain.
- Big data and data analytics. Digital media, and all the related big data that it generates, has created numerous opportunities for marketing professionals to analyse how effective campaigns are. While it enables identification of the inherent value of different omnichannel approaches, oversaturation can also mean that marketers have difficulty determining which data sets are worth monitoring.
- AI platform solutions. Marketing teams can now access central, AI-powered platforms to more effectively manage large data sets. This is transformative in attempting to gain insight into marketing intelligence regarding target audiences. With this information, informed, data-driven decisions can be made about where to focus marketing efforts and marketing budgets to effectively engage certain demographics.
AI in marketing is prolific: it is foundational to search engines, website design, content creation, predictive customer service, speech recognition, targeted ads, and much more.
Netflix is one of the most well-known proponents of AI and service development. Part of its phenomenal success is its ability to serve relevant, intuitive TV and movie recommendations to users, as big data and machine learning use audience preferences to offer personalised content. And there are plenty of subtle ways in which the streaming giant utilises AI; for example, Netflix uses image personalisation to analyse which thumbnail imagery and clips garner the most customer interactions, tailoring the marketing of shows accordingly.
Another global behemoth well-known for its early adoption of AI is Amazon. These technologies are now baked into its operational model; it is believed that 35% of Amazon’s total sales are generated from recommendation engines. AI technologies populate its warehouses with smart robots, continuous AI analyses customer search queries to identify why audiences are searching for particular products, and conversational AI platform, Alexa, learns more about consumer preferences with every interaction.
AI trends for 2022 are predicted to include:
- Large language models to impact conversational AI
- AI solutions applied to boost cybersecurity
- The use of multimodal AI
- New, verticalised AI solutions managed by platform providers
- Increased demand for responsible AI
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